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eleonorarocchi

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Setting Up a Local AI Coding Agent with Ollama and Aider

#ai #llm

Over the past few months, I've experimented with several workflows for using LLMs in software development. In this article, I describe the local setup I've validated on my own PC: Ollama running on Windows, Aider inside Ubuntu on WSL2, and a code-focused model running entirely on the local machine.

The machine I used for my first experiment runs Windows 11 with an Intel Core i7-1355U CPU, 48 GiB of RAM, and no dedicated NVIDIA GPU. It's generally a capable machine, but not particularly well suited for AI inference. Because it lacks a dedicated GPU, I had to run the model in CPU-only mode.

The goal of this setup was to create an environment where both the source code and the data remain entirely on the local machine—far from the cloud-based frontier model approach.

From an architectural standpoint, I decided to keep Ollama on Windows because I also use the installed models with other Windows applications, and I wanted to avoid maintaining duplicate model installations. Aider, on the other hand, runs inside WSL, where installation and configuration are simpler and the Linux command-line environment is more convenient.

Ollama remains exposed on the local port 11434. Aider, running inside Ubuntu WSL2, connects to Ollama through the OLLAMA_API_BASE environment variable.

This approach avoids duplicating models inside WSL while keeping development repositories in the Linux filesystem under ~/repos, where Git and other command-line tools work much more naturally.

Local Setup

Since this machine is not an AI workstation, the solution had to be realistic. That meant avoiding massive GPU-hosted models, cloud APIs, and overly complex automation.

The final architecture looks like this:

Windows
 ├─ Ollama
 │   └─ qwen2.5-coder:14b
 │
 └─ WSL Ubuntu
     ├─ Aider
     ├─ Git
     └─ Development repositories
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Keeping Ollama on Windows while running Aider inside WSL was a natural choice. I already use Ollama with several Windows applications, so duplicating the models inside WSL made little sense. At the same time, I much prefer working with the Linux command line for software development.

Installing Ollama on Windows

I installed Ollama on Windows and downloaded the primary coding model:

ollama pull qwen2.5-coder:14b
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I also installed the smaller model for quicker testing:

ollama pull qwen2.5-coder:7b
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To verify the installation:

ollama list
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The WSL-to-Windows Connectivity Issue

Initially, Aider running inside WSL couldn't communicate with Ollama running on Windows.

To make it work, I had to configure WSL to use mirrored networking.

From PowerShell:

notepad $env:USERPROFILE\.wslconfig
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Then I added the following configuration:

[wsl2]
networkingMode=mirrored
dnsTunneling=true
firewall=true
autoProxy=true
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Finally, I restarted WSL:

wsl --shutdown
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After reopening Ubuntu, I tested the connection:

curl http://127.0.0.1:11434/api/tags
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The response confirmed that WSL could successfully reach the Ollama server running on Windows at 127.0.0.1:11434.

Installing Aider in WSL

Inside Ubuntu/WSL:

sudo apt update
sudo apt install -y git python3 python3-pip python3-venv pipx curl
python3 -m pipx ensurepath
exec $SHELL -l
pipx install aider-chat
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To verify the installation:

aider --version
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I also configured Git so I could version-control my experiments:

git config --global user.name "xxxx"
git config --global user.email "xxxxxxx@xxxxx.xx"
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Creating the First Repository

I created a simple test project:

mkdir -p ~/repos
cd ~/repos
mkdir my-project
cd my-project
git init
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Then I launched Aider:

cd ~/repos/my-project
OLLAMA_API_BASE=http://127.0.0.1:11434 aider --model ollama_chat/qwen2.5-coder:14b --no-show-model-warnings
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When Aider displayed:

Aider v0.86.2
Model: ollama_chat/qwen2.5-coder:14b
Git repo: .git
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I finally knew the basic setup was working correctly.

Opening the Repository from Windows Explorer

For convenience, I also wanted to access the repository directly from Windows Explorer.

From inside WSL:

explorer.exe .
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This command opens the current WSL directory directly in Windows File Explorer.

What's Next?

Once I had Aider working with a local Ollama instance, there was still one thing I wanted to understand better: what was happening under the hood. How many tokens were being processed? How long did inference take? What kind of throughput could I expect?

We'll cover all of that in the next installment. 😉

Top comments (1)

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michael_salinas_472fbf6c1 profile image
Michael Salinas •

Thank you for sharing such an excellent post. I really enjoyed reading it.

I’m a Python Full-Stack Engineer with over 10 years of experience designing and building scalable software solutions for clients across a variety of industries. Along the way, I’ve learned that successful projects depend not only on strong technical execution but also on creating real business value.

With my recent contract completed, I’m exploring new opportunities to collaborate with professionals who value innovation, practical problem-solving, and long-term partnerships. I enjoy discussing ideas that combine technical excellence with sound business strategy, creating outcomes that benefit everyone involved.

I believe every connection has the potential to become something meaningful. If you're interested in exchanging ideas, exploring opportunities, or simply connecting with someone who enjoys building impactful technology, I'd be happy to hear from you.

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